We are continuing to invent, develop, and translate novel Magnetic Resonance (MR) based water displacement imaging methods from the bench to bedside. Specifically, we continue to develop new ways to assess tissue structure and architecture in vivo and non-invasively, with the aim of enabling applications in the neuroscience and biomedical research communities, and translating these approaches to the clinic. Diffusion Tensor MRI (DT-MRI or DTI) is perhaps the best-known imaging method that we invented, developed, and successfully translated clinically. It measures and maps a diffusion tensor of mobile water in tissues. Scalar parameters DTI produces are intrinsic features of the tissue measured without introducing contrast agents or dyes. Endogenous tissue water protons are the species we measure. One DTI-derived quantity, the orientationally-averaged diffusion coefficient (or mean ADC), is the most successful imaging parameter developed to date to visualize an acute stroke in progress. It is also widely used in cancer imaging applications worldwide to monitor cellularity. Our development on novel diffusion anisotropy metrics, like the Fractional Anisotropy (FA) enabled white matter pathways to be visualized. The development direction-encoded color (DEC) maps of axon orientation by Sinisa Pajevic and Carlo Pierpaoli allowed us to map white matter pathway orientation. DEC maps first revealed the main association, projection, and commissural white matter pathways in the human brain. To assess anatomical connectivity between different functional regions in the brain, we invented, proposed and developed DTI streamline tractography, made possible by Sinisa Pajevic and Akram Aldroubi who helped develop and implement a general mathematical framework for obtaining a continuous, smooth approximation to measured discrete, noisy, diffusion tensor field data. Collectively, these methods and approaches have enabled detailed anatomical and structural analyses of the brain in vivo, which was only possible previously using laborious, invasive histological methods performed on excised tissue specimen. Our contributions to the invention and development of streamline tractography was an impetus for the creation of NIH's Human Connectome Project (HCP). As DTI migrated to large, multi-center and multi-patient studies, we began developing a battery of quantitative statistical tests to determine the statistical significance of differences observed in our data. We developed empirical Monte Carlo and Bootstrap methods for determining features of the statistical distribution of the diffusion tensor from experimental DTI data. Another innovation was a novel tensor-variate Gaussian distribution that describes the variability of the diffusion tensor in an ideal DTI experiment, and can be used to optimize the design of DTI experiments. More recently, we developed approaches to measure uncertainties of many tensor-derived quantities, including the direction of axonal pathways using perturbation and statistical approaches. These developments collectively provide the foundation for applying powerful statistical hypothesis tests to address a wide array of important biological and clinical questions that previously could only be tackled in an ad hoc manner, if at all. More recently, we have been developing sophisticated mathematical/physical models of water diffusion profiles and related these to the MR signals that we measure, with the aim of using our MRI data to drill down into the voxel to infer new microstructural and architectural features of tissue (primarily white matter in the brain). One example of this is our composite hindered and restricted model of diffusion (CHARMED) MRI framework which measures a mean axon radius within a pack of axons, and an estimate of the intra and extracellular volume fractions. A refinement of CHARMED, AxCaliber MRI, allows us to measure the axon diameter distribution (ADD) within an axon bundle. Sophisticated multiple pulsed field gradient (PFG) NMR and MRI sequences, developed by Michal Komlosh, and translated by Alexandru Avram, help us characterize microscopic anisotropy within tissues like gray matter that are macroscopically isotropic (like a homogeneous gel). Dr. Komlosh and Ferenc Horkay developed physical MRI phantoms to test and interrogate mathematical models derived by Evren Ozarslan and Dan Benjamini of water diffusion in complex tissues. Dr. Ozarslan also developed novel ways to infer features of size, shape, and distribution of pores in biological tissue and other porous media from their MR data. He also used the theory of fractals to characterize anomalous diffusion processes that are indicative of an underlying hierarchical structure. Collectively, parameters derived from these novel measurements may provide novel sources of MR contrast for neuroscience applications, such as in vivo (Brodmann or cytoarchitechtonic) parcellation of the cerebral cortex or clinical diagnostic applications, such as mild TBI detection, improved cancer diagnosis or brain tumor staging. An important development has been a way to characterize non-Gaussian features of the displacement distribution measured using MRI. To this end, our group continues to work on reconstructing the average propagator (displacement distribution) and features derived from it, using a relatively small number of DWIs to enable their clinical migration. The average propagator is the holy grail of displacement or diffusion imaging, which subsumes DTI as well as other higher-order tensor (HOT) methods. One approach we used previously was an iterative reconstruction scheme along with a priori information and physical constraints to infer the average propagator from DWI data. Another approach was to use CT-like reconstruction method to estimate the displacement profile from DWI data. The most successful method to date, however, developed by Evren Ozarslan, uses special Hermite basis functions to represent the average propagator, which compresses the amount of DWI data required while providing a plethora of new imaging parameters or stains with which to characterize microstructural features in tissues. More recently, a direction our group has pursued actively is inferring function from structure. This entails using our structural MRI data and inferring function or performance of large-scale brain networks. Our work in this area has resulted in the awarding of an R24 grant under the auspices of the NIH BRAIN Initiative. A significant new initiative in our group has been the invention and development of rapid 2D-MRI relaxometry/diffusometry/exchange methods. These can help reveal previously invisible features of tissue, such as the size of different microscopic compartments (e.g., myelin water, myelin) and dynamic relaxation processes occurring in them. Collectively, these novel methods and methodologies represent a pathway to realizing in vivo MRI histology--providing detailed microstructural and microarchitectural information about cells and tissues that otherwise could only be obtained using laborious and invasive histological or pathological techniques applied on biopsied or excised specimens. We are migrating the field of microstructure imaging to microstructure and microdynamic imaging, and in the process, are making the invisible visible.